removing references

README.md

@@ -44,16 +44,15 @@ Alternatively, run `pip install git+https://github.com/facebookresearch/habitat-
 SplitNet's codebase is known to be compatible with https://github.com/facebookresearch/habitat-api/commit/7015813aefae99233864c4ffcf7e52e9097392a4
 
 ## Data
-We use the data sources linked from the public habitat-api repository. You will need to individually download SUNCG, MP3D, and Gibson from their sources. [habitat-sim](https://github.com/facebookresearch/habitat-sim#datasets) and [habitat-api](https://github.com/facebookresearch/habitat-api#data) share the links to the files. We additionally use the Point-Nav datasets from habitat-api, but we also provide a script for generating new datasets.
-Note: The SUNCG assets are no longer available for download, but we will keep our datasets up in case people already have the data.
+We use the data sources linked from the public habitat-api repository. You will need to individually download MP3D, and Gibson from their sources. [habitat-sim](https://github.com/facebookresearch/habitat-sim#datasets) and [habitat-api](https://github.com/facebookresearch/habitat-api#data) share the links to the files. We additionally use the Point-Nav datasets from habitat-api, but we also provide a script for generating new datasets.
 
 To use the same data from the dataset:
 1. Create a symlink to where you downloaded the directory containing the `scene_datasets` asset files for each of the datasets. Call this folder `data`.
 ```bash
 ln -s /path/to/habitat/data data
 ```
 1. Verify that this is set up in the expected way by running `ls data/scene_datasets`. You should see:
-`gibson  mp3d  suncg` based on which you have downloaded.
+`gibson  mp3d` based on which you have downloaded.
 1. Download and extract the premade training datasets by running `sh download_dataset.sh`.
 1. Copy/Move the downloaded datasets into the data folder.
 ```bash

arguments.py

@@ -109,7 +109,7 @@ def get_args():
     )
 
     parser.add_argument(
-        "--dataset", type=str, default="mp3d", help="Picks which dataset to load: mp3d | gibson | suncg", required=True
+        "--dataset", type=str, default="mp3d", help="Picks which dataset to load: mp3d | gibson", required=True
     )
 
     parser.add_argument(

base_habitat_rl_runner.py

@@ -117,9 +117,7 @@ def setup(self, create_decoder):
             if "SEMANTIC_SENSOR" in extra_agent_sensors:
                 extra_task_sensors.append("CLASS_SEGMENTATION_SENSOR")
 
-            if self.shell_args.dataset == "suncg":
-                data_path = "data/datasets/pointnav/suncg/v1/{split}/{split}.json.gz"
-            elif self.shell_args.dataset == "mp3d":
+            if self.shell_args.dataset == "mp3d":
                 data_path = "data/datasets/pointnav/mp3d/v1/{split}/{split}.json.gz"
             elif self.shell_args.dataset == "gibson":
                 data_path = "data/datasets/pointnav/gibson/v1/{split}/{split}.json.gz"
@@ -155,19 +153,13 @@ def setup(self, create_decoder):
                 config.ENVIRONMENT.MAX_EPISODE_STEPS = 250
                 config.TASK.TOP_DOWN_MAP.DRAW_SOURCE_AND_TARGET = False
                 self.env_types.append(ExplorationRLEnv)
-                if self.shell_args.dataset == "suncg":
-                    config.TASK.NUM_EPISODES_BEFORE_JUMP = 5
-                else:
-                    config.TASK.NUM_EPISODES_BEFORE_JUMP = 5
+                config.TASK.NUM_EPISODES_BEFORE_JUMP = 5
             elif self.shell_args.task == "flee":
                 config.TASK.COLLISION_REWARD = 0  # -0.1
                 config.ENVIRONMENT.MAX_EPISODE_STEPS = 250
                 config.TASK.TOP_DOWN_MAP.DRAW_SOURCE_AND_TARGET = False
                 self.env_types.append(RunAwayRLEnv)
-                if self.shell_args.dataset == "suncg":
-                    config.TASK.NUM_EPISODES_BEFORE_JUMP = 5
-                else:
-                    config.TASK.NUM_EPISODES_BEFORE_JUMP = 5
+                config.TASK.NUM_EPISODES_BEFORE_JUMP = 5
             else:
                 raise NotImplementedError("Unknown task type")
 

configs/habitat_nav_task_config.yaml

@@ -34,5 +34,5 @@ SIMULATOR:
     TYPE: HabitatSimSemanticSensor
 DATASET:
   TYPE: PointNav-v1
-  DATA_PATH: data/datasets/pointnav/suncg/v1/{split}/{split}.json.gz
+  DATA_PATH: data/datasets/pointnav/mp3d/v1/{split}/{split}.json.gz
 

dataset_dump/create_pointnav_dataset.py

@@ -18,7 +18,7 @@
 DEBUG = False
 
 DATA_SPLIT = "train"
-DATASET = "suncg"
+DATASET = "mp3d"
 CFG = "configs/habitat_nav_task_config.yaml"
 
 if DATA_SPLIT == "train":
@@ -48,8 +48,6 @@
 # get list of all scenes
 if DATASET == "mp3d":
     scenes = sorted(glob.glob("data/scene_datasets/mp3d/*"))
-elif DATASET == "suncg":
-    scenes = sorted(glob.glob("data/scene_datasets/suncg/house/*"))
 elif DATASET == "gibson":
     scenes = sorted(glob.glob("data/scene_datasets/mp3d/*"))
 else:
@@ -75,8 +73,6 @@
 for ii, house in enumerate(scenes):
     if DATASET == "mp3d":
         scene_id = house + os.sep + house + ".glb"
-    elif DATASET == "suncg":
-        scene_id = (house + "/house.json",)
     elif DATASET == "gibson":
         scene_id = house + ".glb"
     else:

download_dataset.sh

@@ -17,8 +17,8 @@ wget https://dl.fbaipublicfiles.com/splitnet/splitnet_dataset.tar
 echo "Unzipping"
 tar -xf splitnet_dataset.tar
 mv splitnet_dataset/* .
-rm -rf splitnet_dataset
-rm -rf splitnet_dataset.tar
+rmdir splitnet_dataset
+rm splitnet_dataset.tar
 echo "Success"
 cd ..
 

download_weights.sh

@@ -17,8 +17,8 @@ wget https://dl.fbaipublicfiles.com/splitnet/splitnet_models.tar
 echo "Unzipping"
 tar -xf splitnet_models.tar
 mv splitnet_models/* .
-rm -rf splitnet_models
-rm -rf splitnet_models.tar
+rmdir splitnet_models
+rm splitnet_models.tar
 echo "Success"
 cd ..
 

eval_splitnet.py

@@ -34,9 +34,7 @@
 
 
 def get_eval_dataset(shell_args, data_subset="val"):
-    if shell_args.dataset == "suncg":
-        data_path = "data/datasets/pointnav/suncg/v1/{split}/{split}.json.gz"
-    elif shell_args.dataset == "mp3d":
+    if shell_args.dataset == "mp3d":
         data_path = "data/datasets/pointnav/mp3d/v1/{split}/{split}.json.gz"
     elif shell_args.dataset == "gibson":
         data_path = "data/datasets/pointnav/gibson/v1/{split}/{split}.json.gz"

eval_splitnet.sh

@@ -4,7 +4,7 @@
 # This source code is licensed under the Creative Commons license found in the
 # LICENSE file in the root directory of this source tree.
 
-DATASET="suncg"
+DATASET="mp3d"
 TASK="pointnav"
 
 export GLOG_minloglevel=2

flee_test.py

@@ -10,7 +10,7 @@
 from reinforcement_learning.nav_rl_env import RunAwayRLEnv, ExplorationRLEnv
 
 data_subset = "val"
-dataset = "suncg"
+dataset = "mp3d"
 max_episode_length = 250
 RESOLUTION = 10
 
@@ -38,9 +38,7 @@ def draw_top_down_map(info, heading, output_size):
     return top_down_map
 
 
-if dataset == "suncg":
-    data_path = "data/datasets/pointnav/suncg/v1/{split}/{split}.json.gz"
-elif dataset == "mp3d":
+if dataset == "mp3d":
     data_path = "data/datasets/pointnav/mp3d/v1/{split}/{split}.json.gz"
 elif dataset == "gibson":
     data_path = "data/datasets/pointnav/gibson/v1/{split}/{split}.json.gz"

supervised_learning/splitnet_pretrain.py

@@ -277,12 +277,12 @@ def main():
     )
 
     sensors = ["RGB_SENSOR", "DEPTH_SENSOR"] + (["SEMANTIC_SENSOR"] if USE_SEMANTIC else [])
-    if args.dataset == "suncg":
+    if args.dataset == "mp3d":
         data_train = HabitatImageGenerator(
             render_gpus,
-            "suncg",
+            "mp3d",
             args.data_subset,
-            "data/dumps/suncg/{split}/dataset_one_ep_per_scene.json.gz",
+            "data/dumps/mp3d/{split}/dataset_one_ep_per_scene.json.gz",
             images_before_reset=1000,
             sensors=sensors,
             transform=train_transforms,
@@ -293,9 +293,9 @@ def main():
 
         data_test = HabitatImageGenerator(
             render_gpus,
-            "suncg",
+            "mp3d",
             "val",
-            "data/dumps/suncg/{split}/dataset_one_ep_per_scene.json.gz",
+            "data/dumps/mp3d/{split}/dataset_one_ep_per_scene.json.gz",
             images_before_reset=1000,
             sensors=sensors,
         )